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6.6 Ensemble learning and random forest

Slides

Notes

Random Forest is an example of ensemble learning where each model is a decision tree and their predictions are aggregated to identify the most popular result. Random forest only select a random subset of features from the original data to make predictions.

In random forest the decision trees are trained independent to each other.

Classes, functions, and methods:

  • from sklearn.ensemble import RandomForestClassifier: random forest classifier from sklearn ensemble class.
  • plt.plot(x, y): draw line plot for the values of y against x values.

Add notes from the video (PRs are welcome)

  • bootstrapping: training on a subset of the observations
⚠️ The notes are written by the community.
If you see an error here, please create a PR with a fix.

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